| Type: | Package |
| Title: | Model-Robust Standardization for Longitudinal Cluster-Randomized Trials |
| Version: | 0.1.0 |
| Description: | Provides estimation and leave-one-cluster-out jackknife standard errors for four longitudinal cluster-randomized trial estimands: horizontal individual average treatment effect (h-iATE), horizontal cluster average treatment effect (h-cATE), vertical individual average treatment effect (v-iATE), and vertical cluster-period average treatment effect (v-cATE), using unadjusted and augmented (model-robust standardization) estimators. The working model may be fit using linear mixed models for continuous outcomes or generalized estimating equations and generalized linear mixed models for binary outcomes. Period inclusion for aggregation is determined automatically: only periods with both treated and control clusters are included in the construction of the marginal means and treatment effect contrasts. See Fang et al. (2025) <doi:10.48550/arXiv.2507.17190>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 4.1.0) |
| Imports: | reformulas, dplyr (≥ 1.1.0), tidyr (≥ 1.3.0), rlang (≥ 1.1.0), tidyselect, gee, lme4 (≥ 1.1-30), ggplot2 (≥ 3.4.0), stats, utils |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| RoxygenNote: | 7.3.3 |
| NeedsCompilation: | no |
| Packaged: | 2026-01-10 14:19:07 UTC; fancy |
| Author: | Xi Fang [aut, cre], Fan Li [aut] |
| Maintainer: | Xi Fang <x.fang@yale.edu> |
| Repository: | CRAN |
| Date/Publication: | 2026-01-15 17:50:06 UTC |
Fit model-robust standardization for longitudinal CRTs
Description
Fit model-robust standardization for longitudinal CRTs
Usage
mrstdlcrt_fit(
data,
formula,
cluster_id = "cluster",
period = "period",
trt = "trt",
method = c("gee", "lmer", "glmer"),
family = c("gaussian", "binomial"),
corstr = "independence",
scale = c("RD", "RR", "OR")
)
Arguments
data |
data.frame with outcome, treatment, period, cluster, covariates. |
formula |
model formula; may include interactions and random effects. |
cluster_id |
cluster id column name. |
period |
period column name. |
trt |
treatment column name (0/1). |
method |
"gee","lmer","glmer". |
family |
"gaussian","binomial". |
corstr |
gee correlation. |
scale |
For binomial: "RD","RR","OR" (RR/OR are on log scale). |
Value
Object of class "mrs".
Plot method for mrs objects
Description
Plot method for mrs objects
Usage
## S3 method for class 'mrs'
plot(x, level = 0.95, estimand = NULL, point_size = 2.8, ...)
Arguments
x |
An object of class |
level |
Confidence level. |
estimand |
Subset of estimands to plot. |
point_size |
Point size. |
... |
Unused. |
Value
ggplot object invisibly.
Print method for mrs objects
Description
Print method for mrs objects
Usage
## S3 method for class 'mrs'
print(x, ...)
Arguments
x |
An object of class |
... |
Unused. |
Value
x invisibly.
Summarize an mrs fit
Description
Summarize an mrs fit
Usage
## S3 method for class 'mrs'
summary(
object,
level = 0.95,
estimand = NULL,
digits = 6,
show_counts = TRUE,
...
)
Arguments
object |
An object of class |
level |
Confidence level. |
estimand |
Optional subset of estimands. |
digits |
Digits to print. |
show_counts |
Print counts tables. |
... |
Unused. |
Value
Invisibly returns a list of printed tables and metadata.
Example stepped wedge CRT dataset with binary outcome
Description
A toy dataset with cluster, period, and individual records for illustrating estimands in stepped wedge CRT with a binary outcome.
Usage
data(sw_b)
Format
A data frame with columns:
- cluster
Cluster identifier (integer).
- period
Period index (integer).
- id
Individual identifier within cluster-period (integer).
- trt
Treatment indicator (0/1).
- x1
Auxiliary covariate (0/1).
- x2
Auxiliary covariate (numeric).
- y
Outcome (0/1, binary).
Examples
data(sw_b)
head(sw_b)
Example of stepped wedge CRT dataset for continuous outcome
Description
A toy dataset with cluster, period, and individual records for illustrating estimands in stepped wedge CRT with a continuous outcome.
Usage
data(sw_c)
Format
A data frame with columns:
- cluster
Cluster identifier (integer).
- period
Period index (integer).
- id
Individual identifier within cluster-period (integer).
- trt
Treatment indicator (0/1).
- x1
Auxiliary covariate (0/1).
- x2
Auxiliary covariate (numeric).
- y
Outcome (numeric, continuous).
Examples
data(sw_c)
head(sw_c)
Example crossover cluster-randomized trial dataset with binary outcome
Description
A small simulated 2×2 crossover trial dataset with a binary outcome.
Usage
xo_b
Format
A tibble/data.frame with one row per subject and the following columns:
- h
Integer cluster ID (hospital)
- p
Integer period (1 or 2)
- k
Integer subject index within cluster-period
- trt
Treatment indicator (0 = control, 1 = treatment)
- x_c01, x_c02
Continuous covariates
- x_b01
Binary covariate (0/1)
- x_cat1_2, x_cat1_3
Dummy variables for a 3-level categorical covariate (level 1 is reference)
- y_bin
Observed binary outcome (0/1)
Examples
data(xo_b)
str(xo_b)
head(xo_b)
Example crossover cluster-randomized trial dataset with continuous outcome
Description
A small simulated 2×2 crossover trial dataset with a continuous outcome.
Usage
xo_c
Format
A tibble/data.frame with one row per subject and the following columns:
- h
Integer cluster ID (hospital)
- p
Integer period (1 or 2)
- k
Integer subject index within cluster-period
- trt
Treatment indicator (0 = control, 1 = treatment)
- x_c01, x_c02
Continuous covariates
- x_b01
Binary covariate (0/1)
- x_cat1_2, x_cat1_3
Dummy variables for a 3-level categorical covariate (level 1 is reference)
- y_cont
Observed continuous outcome
Examples
data(xo_c)
str(xo_c)
head(xo_c)